TY - GEN
T1 - Designing subspecies of hardware trojans and their detection using neural network approach
AU - Inoue, Tomotaka
AU - Hasegawa, Kento
AU - Kobayashi, Yuki
AU - Yanagisawa, Masao
AU - Togawa, Nozomu
N1 - Funding Information:
ACKNOWLEDGMENTS This paper is based on the results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO).
Funding Information:
This paper is based on the results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO).
Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/13
Y1 - 2018/12/13
N2 - Due to the recent technological development, home appliances and electric devices are equipped with high-performance hardware device. Since demand of hardware devices is increased, production base become internationalized to mass-produce hardware devices with low cost and hardware vendors outsource their products to third-party vendors. Accordingly, malicious third-party vendors can easily insert malfunctions (also known as 'hardware Trojans') into their products. In this paper, we design six kinds of hardware Trojans at a gate-level netlist, and apply a neural-network (NN) based hardware-Trojan detection method to them. The designed hardware Trojans are different in trigger circuits. In addition, we insert them to normal circuits, and detect hardware Trojans using a machine-learning-based hardware-Trojan detection method with neural networks. In our experiment, we learned Trojan-infected benchmarks using NN, and performed cross validation to evaluate the learned NN. The experimental results demonstrate that the average TPR (True Positive Rate) becomes 72.9%, the average TNR (True Negative Rate) becomes 90.0%.
AB - Due to the recent technological development, home appliances and electric devices are equipped with high-performance hardware device. Since demand of hardware devices is increased, production base become internationalized to mass-produce hardware devices with low cost and hardware vendors outsource their products to third-party vendors. Accordingly, malicious third-party vendors can easily insert malfunctions (also known as 'hardware Trojans') into their products. In this paper, we design six kinds of hardware Trojans at a gate-level netlist, and apply a neural-network (NN) based hardware-Trojan detection method to them. The designed hardware Trojans are different in trigger circuits. In addition, we insert them to normal circuits, and detect hardware Trojans using a machine-learning-based hardware-Trojan detection method with neural networks. In our experiment, we learned Trojan-infected benchmarks using NN, and performed cross validation to evaluate the learned NN. The experimental results demonstrate that the average TPR (True Positive Rate) becomes 72.9%, the average TNR (True Negative Rate) becomes 90.0%.
KW - design time
KW - gate-level netlist
KW - hardware Trojan
KW - machine learning
KW - neural network
UR - http://www.scopus.com/inward/record.url?scp=85060285069&partnerID=8YFLogxK
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U2 - 10.1109/ICCE-Berlin.2018.8576247
DO - 10.1109/ICCE-Berlin.2018.8576247
M3 - Conference contribution
AN - SCOPUS:85060285069
T3 - IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin
BT - 2018 IEEE 8th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018
PB - IEEE Computer Society
T2 - 8th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018
Y2 - 2 September 2018 through 5 September 2018
ER -